A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 5 Issue 2
Mar.  2018

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Hongjun Yang and Jinkun Liu, "An Adaptive RBF Neural Network Control Method for a Class of Nonlinear Systems," IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 457-462, Mar. 2018. doi: 10.1109/JAS.2017.7510820
Citation: Hongjun Yang and Jinkun Liu, "An Adaptive RBF Neural Network Control Method for a Class of Nonlinear Systems," IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 457-462, Mar. 2018. doi: 10.1109/JAS.2017.7510820

An Adaptive RBF Neural Network Control Method for a Class of Nonlinear Systems

doi: 10.1109/JAS.2017.7510820
Funds:

the National Natural Science Foundation of China 61703402

the National Natural Science Foundation of China 61374048

More Information
  • This paper focuses on designing an adaptive radial basis function neural network (RBFNN) control method for a class of nonlinear systems with unknown parameters and bounded disturbances. The problems raised by the unknown functions and external disturbances in the nonlinear system are overcome by RBFNN, combined with the single parameter direct adaptive control method. The novel adaptive control method is designed to reduce the amount of computations effectively. The uniform ultimate boundedness of the closed-loop system is guaranteed by the proposed controller. A coupled motor drives (CMD) system, which satisfies the structure of nonlinear system, is taken for simulation to confirm the effectiveness of the method. Simulations show that the developed adaptive controller has favorable performance on tracking desired signal and verify the stability of the closed-loop system.

     

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